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Robust kernel center matrix, robust kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A, Fukumizu, K., Wang Y.-P.

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MBBYuPingWang/RKUM-Robust-Kernel-Unsupervised-Methods

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RKUM-Robust-Kernel-Unsupervised-Methods

Robust kernel center matrix, robust kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A, Fukumizu, K., Wang Y.-P. (2018);http://doi:10.1016/j.neucom.2018.04.008 Alam, M. A, Calhoun, C. D., Wang Y.-P. (2018); doi:10.1016/j.csda.2018.03.013

  • Version: 0.1.1

  • Imports: stats, graphics

  • Published: 2019-03-04

  • Author: Md Ashad Alam

  • Maintainer: Md Ashad Alam

  • License: GPL-3

  • NeedsCompilation: no

  • CRAN checks: RKUM results

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.1.1 2.98 31.41 34.39 OK
r-devel-linux-x86_64-debian-gcc 0.1.1 2.44 24.01 26.45 OK
r-devel-linux-x86_64-fedora-clang 0.1.1 40.23 OK
r-devel-linux-x86_64-fedora-gcc 0.1.1 38.08 OK
r-devel-windows-ix86+x86_64 0.1.1 9.00 64.00 73.00 OK
r-patched-linux-x86_64 0.1.1 2.17 29.09 31.26 OK
r-patched-solaris-x86 0.1.1 55.60 OK
r-release-linux-x86_64 0.1.1 2.29 29.01 31.30 OK
r-release-windows-ix86+x86_64 0.1.1 5.00 50.00 55.00 OK
r-release-osx-x86_64 0.1.1 OK
r-oldrel-osx-x86_64 0.1.1 OK

Downloads:

  • Reference manual: RKUM.pdf

  • Package source: RKUM_0.1.1.tar.gz

  • Windows binaries: r-devel: RKUM_0.1.1.zip, r-release: RKUM_0.1.1win.zip

  • OS X binaries: r-release: RKUM_0.1.1.tgz, r-oldrel: RKUM_0.1.1os.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=RKUM to link to this page.

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Robust kernel center matrix, robust kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A, Fukumizu, K., Wang Y.-P.

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